AI agents are gaining attention like never before. Thanks to recent advances in AI technology, it’s now possible to implement them with practical levels of accuracy. That’s why many are calling 2025 “the year of AI agents.” Major players such as Microsoft, Google, and IBM are already developing and offering AI agent–based services.
In this blog, we’ll take a closer look at this emerging AI technology—covering what AI agents are, how they work, and how they can be applied in real-world business scenarios.
Table of Contents
1. What is an AI Agent?
To better understand AI agents, let’s first outline what they are, why they matter, and how they differ from generative AI.
1-1. Overview
An AI agent is a system built on AI technology that can autonomously and continuously carry out tasks in order to help users achieve their goals.
AI, at its core, refers to a broad set of technologies that enable computers to learn from data and behave intelligently. Up until now, AI systems have mainly generated outputs based on pre-trained data and user instructions. In contrast, an AI agent can take an abstract instruction, interpret the situation, gather the necessary data on its own, and then perform the steps required to complete the task.
By enabling this autonomy, AI agents expand the potential use cases of AI and are expected to play a key role in many business scenarios.
1-2. Why AI Agents are Getting Attention
The concept of agents in AI isn’t new—research was already being conducted in the 1990s. But limited computing power held back practical applications.
Fast-forward to today: breakthroughs in generative AI like ChatGPT have made it possible to handle a wide range of general-purpose tasks. At the same time, advances in processing units such as TPUs (Tensor Processing Unit) and AI-specific chips have raised computing power to the level needed to bring AI agents to life with practical accuracy and cost efficiency.
1-3. AI Agents vs. Generative AI
So how exactly do AI agents differ from generative AI? Here’s a side-by-side look at their key differences and what each is designed to do.
AI Agent Generative AI Main Role
- Executes tasks based on input
- Generates text, images, or other content based on input
Autonomy
- Executes tasks autonomously by combining multiple tools and methods
- Provides outputs based on input but does not make independent decisions
Adaptability
- Dynamically adjusts methods and data sources depending on context (e.g., day, date, weather)
- Produces outputs based on fixed learned data
Generative AI produces results based only on training data. AI agents, on the other hand, analyze what’s needed, break tasks down into steps, and execute them autonomously. In other words, AI agents make generative AI more dynamic and capable of real-world task execution.
2. How AI Agents Work
While models differ, AI agents typically follow three steps: breaking down tasks, reasoning about how to complete them, and executing them.
2-1. Breaking Down Tasks
An AI agent starts by dividing a user’s request into subtasks.
For example, if asked to “create this month’s sales report,” the agent might break this into:
・Retrieve updated sales rep information
・Gather current sales opportunity data
・Load the predefined report format
・Consolidate and generate a progress report
Large Language Models (LLMs), the foundation of generative AI, are often used to enable this breakdown.
2-2. Reasoning About Execution
The AI agent then decides which tools to use and in what order.
For example, it may pull sales data from a CRM system and use a BI tool to format and visualize the report. This reasoning capability allows agents to handle complex tasks without human intervention.
2-3. Carrying Out Tasks
Finally, the AI agent executes the tasks.
In the above case, it would access the CRM, retrieve the data, process it in a BI tool, and generate the report.
These steps are iterative: if unexpected data appears, the AI agent may adjust its approach and attempt another method to complete the task.
3. What Can AI Agents Do?
So how can AI agents actually be put to work in a business setting? Below are some concrete use cases that show where AI agents can add value.
3-1. User Assistance Support
In areas like internal help desks or customer support, AI agents can take on the full process of identifying a user’s issue, pulling answers from FAQs or internal systems, and delivering a clear response. Unlike a simple chatbot, an AI agent can understand context, gather relevant information, and respond accordingly—leading to higher user satisfaction.
3-2. Sales Support
From lead generation, personalized proposals, and activity tracking to planning the next outreach, AI agents can assist across the entire sales process. For example, they can analyze a customer’s purchase history and inquiries to create tailored marketing emails.
3-3. Business Continuity Planning (BCP)
AI agents can help identify potential risks, design response scenarios, and check compliance with regulations. For instance, they can cross-reference facility locations with hazard maps to quickly assess potential threats.
3-4. HR Management
By analyzing employee data, evaluating engagement, and supporting career development and transfer planning, AI agents provide valuable insights. For example, they might combine performance data with manager notes to generate more objective evaluations.
Summary
AI agents are among the most important technologies to watch today. They can automate routine tasks, boost productivity, and improve work quality across the enterprise.
One way to start is by using Microsoft Copilot Studio, which allows organizations to design AI agents that gather information, trigger workflows, and execute tasks automatically. A free trial is available, making it easy to experiment.
At SYSCOM GLOBAL SOLUTIONS, we’ve been delivering tailored IT solutions as a Microsoft Solutions Partner for over 30 years. Our experts stay up-to-date on the latest Copilot advancements and can help you design the right solution for your business.
Contact us today to learn more.